Card for the telearms-gym rebuild

README.md

@@ -26,11 +26,11 @@ root = os.environ.get("MINARI_DATASETS_PATH", os.path.expanduser("~/.minari/data
 snapshot_download("mliu48/telearms", repo_type="dataset", local_dir=os.path.join(root, "telearms"),
                   allow_patterns="CubePatchPlacement/random-1500-v0/*")
 dataset = minari.load_dataset("telearms/CubePatchPlacement/random-1500-v0")
-env = dataset.recover_environment()  # needs the repo's gymnasium_env/ on the path
+env = dataset.recover_environment()  # needs telearms-gym installed
 ```
 
 The environment is `TeleArmsEnv(task, obs_mode="privileged")` in
-[gymnasium_env/telearms_env.py](https://github.com/helen9975/RoboEval-telearms/tree/gymnasium-env/gymnasium_env). On a machine without EGL, set `MUJOCO_GL=disable`
+[telearms-gym](https://github.com/liuwilli10101/telearms-gym) (`gymnasium_env/telearms_env.py`). On a machine without EGL, set `MUJOCO_GL=disable`
 before importing it.
 
 **Observations** are the simulator's state, not images: the robot's 14 joint and gripper values,
@@ -55,7 +55,7 @@ is the sparse reward, 1.0 on the step that reaches success, else 0. `infos["rewa
 environment's `reward_type="dense"` reward: 1/N on the first step that reaches each of the task's
 N stages (grasp, lift, place, ...) plus 1.0 on success, at most 2 per episode. Each demo ends at
 its first success (`terminations`), as the environment ends an episode. Of the 45,000 demos,
-1.5% were accepted when played but never satisfy the current task check; they end with
+665 (1.5%) were accepted when played but never satisfy the environment's task check; they end with
 `truncations` and no reward.
 
 The same demos with camera images are the `offline-rl` branch of